Original Scientific Article Relevance of the World Economic Forum Tourism Competitiveness Index for International Association Events: The Case of New EU Member States Jan Oršič and Blaž Bregar jan.orsic@gmail.com bregarb@gmail.com This paper focuses on the most lucrative type of tourism: business tourism, particularly association events and the effects of specific factors on the process of selecting destinations for these events. The research is based on various statistics to comprehensively establish a connection between association events and the international competitiveness of 13 countries that joined the EU in or after 2004. Even though they all vary in size, development and geographic location within Europe, the results show that the countries' economies and association events have a strong connection and that positive influences of this segment of tourism can be felt throughout the destinations. Key words: Association events, international competitiveness, destination marketing organisations, new Europe Introduction Tourism is the world's largest industry (Donyadi-deh, 2013; Goutam Panigrahi, 2012), and international meetings and events represent one of its most lucrative segments, in addition to being considered global generators of added value (Kim et al., 2003) for participants and destinations alike. They increase the added value of tourism, generate revenue from business guests, diversify destination tourism services and facilities and, consequently, decrease destination seasonality (Davidson & Rogers, 2006; Rudez, Sedmak & Bojnec, 2013). The largest subcategory of business travel, meetings and events are association events. There is an entire industry dedicated to attracting, facilitating and organizing association events from hotels to meeting planners (Karin Weber, 2001). Interestingly, this is not a well-researched field of tourism, and the purpose of this study was to offer an overview of research on association events on an international level. To do so, we chose to analyse the 13 countries that joined the European Union in or after 2004: Bulgaria, Croatia, Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovakia, and Slovenia were selected for comparison as they often compete for the same events and are frequently compared for geographical or historical reasons and are often referred to as the "New Europe" (Dunford, 2005; Schimmelfennig, 2000). We will investigate the relevance of the World Economic Forum (WEF) Travel and Tourism Competitiveness Index (TTCI), particularly its sub-indexes, in an attempt to link it to the association market. Association Events, Destination Selection Process, Competitiveness and the ICCA Statistics The competition for association events is fierce, as this is one of the fastest growing and most lucrative Académica Turística, Year 8, No. 2, November 2015 | 45 Jan Oršič and Blaž Bregar Relevance of the World Economic Forum Tourism Competitiveness Index segments of the tourism industry (Chen, 2006, p. 167). The destination selection process can take several years, and organizers carefully compare and assess every element that each destination has to offer; consequently, destinations face tough competition with decisions depending on the smallest details (Richards, 2011; H. E. Chacko & Fenich, 2000). This process can take anywhere from one to several years. We wanted to see if there are other internationally comparable competitiveness factors apart from the general determining ones like logistics, accommodation and congress capacities (Sikosek, 2012) that we could use for our research. The destination selection process usually begins with international associations publishing tenders for the organisation of their future events. The success of an event is crucial for the future development of the association and can have major benefits for the destination (Crouch & Brent Ritchie, 1997). Given that destinations are recognized as the primary unit of analysis in tourism research and have been well researched (Line & Runyan, 2014; Pike & Page, 2014; Davidson & Keup, 2012; Pike, Murdy & Lings, 2010), one option would be to look at the destination marketing organisations (DMOs). These play a major role in promoting their destinations with associations, working with the local association chapters and obtaining financial subsidies for international events (Morrison, 2013). That is why the bigger the event is, the more assistance is expected from the national or local DMO (Wang, 2008). This is often crucial in the destination selection process (Crouch & Brent Ritchie, 1997, p. 63) but no comparable information has been found as assistance is not always made public. An additional problem is also that the term 'DMO' includes tourist boards, convention and visitor bureaus, and a number of other organisations dedicated to leisure and business travels and which are usually fully or partly governmental institutions attempting to cover all the different segments of tourism that are relevant to a destination (Zavattaro & Adams, 2015; Pike & Page, 2014). Authors researching business travel agree that the number of publications in the field is quite limited, especially in the context of its global financial impact (Beaverstock & Budd, 2013; Lyons, 2013; Faul-conbridge, Beaverstock, Derudder & Witlox, 2009). Business travel, including association and corporate meetings and events, even lacks a clear definition. It has often been described by the abbreviation MICE, which stands for meetings, incentives, congresses, and events or exhibitions (Vanneste, 2008; Weber & Ladkin, 2003; Lawrence & McCabe, 2001). Some use the abbreviation SMERF (social, military, educational, religious and fraternal events) (Davidson & Rogers, 2006), others break it down to meetings (e.g. conferences, symposiums, board meetings, and committee meetings), business events (tradeshows, sales training, product launches, customer events, and incentives) and social events (weddings, reunions, and civic and community events) (Sperstad & Cecil, 2011). Association events cover all these definitions. The process of international destination selection for association events is also not academically well documented or researched (Crouch, 2004; Jun & Mc-Cleary, 1999). Crouch and Brent Richie (1997) have set a model for researching site selection factors, which has been used as a backbone of later research focusing on the site level (Draper, Dawson & Casey, 2011; Fawzy & Samra, 2008; Todorovic & Zavrsnik, 2002) and city level (Chacko & Fenich, 2000; Nelson & Rys, 2000; Upchurch, Jeong, Clements & Jung, 2000). As far as the country level in reference to association events is concerned, research is more limited and is clearly focused on the predominantly financial aspect of the industry value for a specific country, following the guidelines set by the World Tourism Organisation Satellite report (Zhang, 2014; VisitDen-mark, 2012; World Tourism Organization, 2006; Baskey, Ross, Patel, Wittman & Daniel, 2008). However, no research was found comparing different countries in reference to the meetings industry. Competitiveness In recent years, competitiveness has become an increasingly significant topic, and there is substantial academic literature analysing different aspects of travel and tourism competitiveness. The term itself is primarily related to international competitiveness followed by that of the industry level and firm level (Bhawsar & Chattopadhyay, 2015). In the case of tourism research, the primary focus has been on the industry level nationally, regionally or locally; therefore, we predominately see implementation empirical surveys focusing on subjective consumer meas- 46 | Académica Turística, Year 8, No. 2, November 2015 Jan Oršič and Blaž Bregar Relevance of the World Economic Forum Tourism Competitiveness Index ures (Sedmak & Kociper, 2013) or complex mathematical models (Barros et al., 2011). If local suppliers connect with each other and start cooperating with DMOs, they can increase their competitiveness and, by adopting digital media, they can reach their desired markets for promoting their destination product (Hays, Page & Buhalis, 2013; Vodeb, 2012; Buhalis, 2000; Minghetti & Buhalis, 2010). Of course, all this reflects the overall competitiveness of a country; here, we can find several examples using the WEF competitiveness reports, such as in a comparison of the 25 top world destinations (Balan, Balaure & Veghes, 2009) and Brazil and Switzerland (Montanari, Giraldi & Campello, 2014). An enquiry of the relationship between research and development (R&D) and competitiveness of South East Europe (SEE) economies has been conducted by Radosevic (2009) although he uses the WEF International competitiveness index, he does look at all of the new member states. It bears mentioning that the WEF was a relevant source for benchmarking country competitiveness in 1979 and captures both the microeconomic and macroeconomic foundations of national competitiveness (Bhawsar & Chat-topadhyay, 2015). With the historic EU enlargement in 2004, ten new member states with the combined population of 75 million citizens joined a community of 15 pre-existing members: Latvia, Lithuania, Estonia, Poland, the Czech Republic, Slovakia, Hungary, Slovenia, Cyprus, and Malta, which are all very similar yet very different (Coles & Hall, 2005). Romania and Bulgaria joined the EU in 2007, and Croatia followed in 2013. These 13 member states are the subject of our research, and we can refer to them as New Europe. We will not use the term Central and Eastern Europe (CEE), as it excludes Cyprus and Malta (Hughes & Allen, 2009; Hall, 1999). By entering the EU and some also the Schengen zone, these states opened their door to European tourism and business. As tourism continues to grow globally and represents a significant source of income for national, regional and local economies, efforts have intensified on the country level to understand the role of DMOs in the international meetings and events market. Despite the importance and interest of the sector, there are only very scarce sources of statistical information that would be dealing with the segment of international meetings and events at an international level. For this reason, we also decided to use the WEF Travel Competitiveness Index, which has three sub-indexes, which were used in the analysis. The sub-indexes are: - Travel and tourism regulatory framework (includes the factors of regulation and politics referring to the environmental sustainability, security, health and safety and prioritisation of tourism). For this sub-index, we will use the abbreviation REG. - Business environment and infrastructure (referring to the factors of air transport infrastructure, public transport infrastructure, tourist infrastructure, ICT infrastructure and price competitiveness in tourism and travel). For this sub-index, we will use the abbreviation BUS. - Travel and tourism human, cultural and natural resources (includes, in addition to the human and natural resources, the issue of cultural affinity for tourism and travel). For this sub-index, we will use the abbreviation HR. It is important to note here that each of these three pillars is calculated as an average of the values that compose them. ICCA Statistics The International Congress and Convention Association was founded in 1963 and currently has almost 100 member organisations worldwide. It focuses on tracking association events globally. In 2010, the ICCA began linking its database of association meeting profiles with the profiles of the Union of International Associations (UIA). The ICCA database only includes events that have at least 50 participants, are organized on a regular basis (one-time events are not included) and rotate between at least three different countries. The association database provides a historical overview of each meeting, creating a complete record of accomplishment of where it was held in the past and the destinations that have been chosen for the future (ICCA n.d.). 46 | Académica Turística, Year 8, No. 2, November 2015 Jan Oršič and Blaž Bregar Relevance of the World Economic Forum Tourism Competitiveness Index Research Question Development The extent of support and preconditions for association events varies and is related to the destinations' dedication to attracting these events in the national tourist and political agenda. However, we assume that associations would prefer destinations with stable economies, local association chapters or the potential to open one. These elements should be reflected in the WEF TTCI indexes. We aim to explore whether the analysed sub-indexes (Regulatory Framework; Business Environment; Human, Cultural and Natural Resources) are connected to the number of events in the selected countries. As Bulgaria, Croatia, Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovakia and Slovenia all met the criteria to enter the EU, thus boosting the interest from international associations to hold events there, can we assume that the difference between them is in their ability to realize the hosting of these events? Research Question 1: The number of international events in a country can be indicated by WEF Travel Competitiveness sub-indexes. The countries joining the EU in or since 2004 all had similar economic baselines. As those countries have been developing recently, reflected in their GDP growth, they would also have improved their attractiveness as a destination for association events, which would be reflected in improved WEF indexes. Can we, therefore, find evidence of GDP growth and improvement in the sub-indexes increased the number of international events? Additionally, are there any differences among the countries observed that would explain the changes in the number of international events and WEF Travel Competitiveness sub-indexes in the observed periods (2007-2013)? Research Question 2: Number of international events reflects a country's growth in GDP and WEF Travel Competitiveness sub-indexes. Method The first challenge was data collection. The main problem was that countries collect different data about tourism, so we were not able to find sufficiently comparable information referring solely to association events. Several associations collect data about association events through member organisa- tions, such as European Cities Marketing (ECM), the Union of International Associations (UIA), Meeting Professional International (MPI), and the International Congress and Convention Association (ICCA). They all collect different types of information that are only accessible to members or based on a special authorisation. The data we looked for is collected by the ICCA, and we were able to acquire it with the help of the Ljubljana Tourism Convention Bureau. Therefore, without their help, this research would not have been possible. From the ICCA database, we acquired data that refers to the number of events and number of different locations in each country where events took place and individually in the time range from 1 January 2006 to 31 December 2013. GDP information per country was taken from Eurostat (2015). For the analysis, we used SPSS Statistic 21. Our first step was an exploratory data analysis and sample investigation, which concluded that the data is suitable for correlation and regression analyses. For the testing of the first hypothesis, we used correlation analysis, specifically the Pearson correlation coefficient (1-tailed). Firstly, the number of events per 1 billion euros of GDP was calculated. This information was then correlated with the three WEF TTCI sub-indexes. To test the second hypothesis, we used multiple linear regression. In our model, the dependent variable was expressed as the percentage change per 1 billion euros of GDP between 2007 and 2013. The independent variables included the percentage change in the three sub-indexes and GDP compound annual growth rate (CAGR) based on the data from Eurostat. The CAGR calculation included GDP at current prices expressed in billions of euros for 2007 and 2013. As the values we were getting were interesting, we decided to perform a cluster analysis of the observed countries based on the index values of the two variables. The first variable was the number of events per billion-euro GDP in 2007 and 2013.The second variable was the two sub-indexes' regulatory frameworks and business environment from the same two years. Then, we looked at the values of variables and compared them across segments. 46 | Académica Turística, Year 8, No. 2, November 2015 Relevance of the World Economic Forum Jan Oršič and Blaž Bregar Tourism Competitiveness Index Findings and Discussion For the first research question, we calculated the correlations between the number of events in individual countries per billion euros and the three WEF sub-indexes. The analysis concluded that there is a statistically significant correlation between the number of events per billion euros of GDP in 2007 with the Regulatory Framework and Business Environment sub-indexes, both at the p< 0.01 significance level. Similarly, for 2013 we found a statistically significant correlation between some events per billion euros of GDP in 2013 and Regulatory Framework (p< 0.01) and Business Environment (p< 0.01). For both 2007 and 2013, no statistically significant correlation was found for the Human, Cultural and Natural Resources sub-index. Therefore, we can partially confirm the first research question, establishing that there is a statistical correlation between the number of events and two out of three WEF sub-indexes. We can, therefore, assess that higher values in these WEF Travel Competitiveness sub-indexes correlates with higher numbers of association events for the countries observed. Although small, the sample works in the countries that differ in size, development and geographical location within Europe. Table 1 Correlations between the number of events per billion euros of GDP and WEF sub-indexes. Events per billion euro of GDP 2007 Events per billion euro of GDP 2013 WEF Sub index Regulatory framework 2007 WEF Sub index Business Environment 2007 WEF Sub index Human, Cultural and Natural resources 2007 WEF Sub index Regulatory framework 2013 WEF Sub index Business Environment 20l3 WEF Sub index Human, Cultural and Natural resources 2013 Events per billion euro of GDP 2007 .95l* .625* .7l5* 0.367 .764* .837* 0.l04 Events per billion euro of GDP 2013 .95l* .564* .735* 0.39 .728** .842* 0.09l WEF Sub index Regulatory framework 2007 .625* .564* .755* .658** .68l** 0.352 WEF Sub index Business Environment 2007 .7l5* .735* .755* .688** .845* .9l4* 0.387 WEF Sub index Human, Cultural and Natural resources 2007 0.367 0.39 .658** .688** .485* .665* .697* l l l Académica Turística, Year 8, No. 2, November 2015 | 49 Jan Oršič and Blaž Bregar Relevance of the World Economic Forum Tourism Competitiveness Index Events per billion euro of GDP 2007 Events per billion euro of GDP 2013 WEF Sub index Regulatory framework 2007 WEF Sub index Business Environment 2007 WEF Sub index Human, Cultural and Natural resources 2007 WEF Sub index Regulatory framework 2013 WEF Sub index Business Environment 2013 WEF Sub index Human, Cultural and Natural resources 2013 .764* .728* .845* .485* .802** 0.3l2 .837* .842* .68l** .9l4* .665* .802** 0.396 0.l04 0.09l 0.352 0.387 .697* 0.3l2 0.396 l WEF Sub index Regulatory framework 2013 WEF Sub index Business Environment 2013 WEF Sub index Human, Cultural and Natural resources 2013 N 13 13 13 13 **. Correlation is significant at the 0.01 level (1-tailed). *. Correlation is significant at the 0.05 level (1-tailed). For the second research question, multiple linear regression was calculated to predict the change in the number of events from 2007 to 2013, based on the change in sub-indexes and GDP. No significant regression equation was found (F (4.8) = 0.965, p = 0.476), with an R2 =0.326. Equally, none of the regression coefficients was found to be statistically significant. Therefore, we can conclude that the relative change in sub-indexes and GDP cannot explain the changes in the number of international events for the observed countries. The multiple regression model consisted of: - Predictors: (Constant), Relative change in sub-index HR (from 2007 to 2013), GDP_CAGR (compounded annual growth rate from 2007 to 2013), Relative change in sub-index Business Environment (from 2007 to 2013), Relative change in sub-index REG (from 2007 to 2013) l3 l3 l3 l3 - Dependent Variable: Relative change in number of events (defined as percentage change from 2007 to 2013) Given that our model showed that the difference in the number of international events throughout the 2007-2013 period could not be explained by GDP growth and changes in the WEF indexes, our purpose is to find meaningful differences between the countries that could explain their specific development. We have split the sample of our countries into three clusters, based on the number of association events in 2013 per billion euros of GDP (BN GDP) and the value of WEF sub-indexes (Regulatory and Business). Based on key development indicators and event statistics, we can further examine differences among them and answer the second research question. Through our analysis, we have ended up with three groups depending on the GDP and number of events. The first group, consisting of Slovakia, Poland, Romania, and Bulgaria, all have less than one event in 2013 per BN GDP. Low WEF index values show the relatively lower level of development, which 46 | Académica Turística, Year 8, No. 2, November 2015 Jan Oršič and Blaž Bregar Relevance of the World Economic Forum Tourism Competitiveness Index Table 2 Number of events per billion euros of GDP per country Table 3 Cluster analysis and OLAP analysis, breakdown of segments. M tí > C 3 K ft U ^ 3 N ft u ^ d o SH u > tí ft es e tí ¡3 O A C 3 O U 00 h ri Z m ft (V > w ft Ph ft z 0 O 2 O M ft ft O Cluster 1 Cluster 2 Cluster 3 Total Latvia, Czech Republic, Hungary, Slovenia, Lithuania, Bulgaria, Slovak Republic, Poland, Romania Cyprus, Estonia, Malta Croatia Events per bil- lion euro of 1.19 0.5 2.59 l.35 GDP 2007 Events per bil- lion euro of 1.28 0.54 2.17 l.32 GDP 2013 Sub index Re- gulatory fra- 4.66 4.08 5.11 4.63 mework 2007 Sub index Bu- siness Environment 3.95 3.39 4.44 3.93 2007 Sub index Re- gulatory fra- 5.1 4.83 5.46 5.l2 mework 2013 Sub index Bu- siness Envi- 4.3 3.92 4.89 4.35 ronment 2013 • Cyprus • Estonia • ::ech Slovenia • Hungary • Croatia • Lithuania 0 Poland • Romania Graph 1 Number of events per GDP, graphic interpretation marks them as not as attractive for international association events. Romania and Bulgaria are the two poorest EU member states. The second group, including the Czech Republic, Slovenia, Latvia, Hungary, Croatia, and Lithuania, is positioned somewhere in the middle, both in WEF indexes and number of association events. The destinations in this group are all well positioned in the association market, and all find themselves in the largest group. 46 | Académica Turística, Year 8, No. 2, November 2015 Jan Oršič and Blaž Bregar Relevance of the World Economic Forum Tourism Competitiveness Index The third group consists of Cyprus, Malta and Estonia, with high WEF indexes and a high number of events per billion euros of GDP, which seem to be the most attractive for the association events. Of the three, Malta is an especially strong destination. We can link this to good infrastructure, good flight connections, and favourable weather conditions in the time of the conference season as well as not having the image of an eastern European country, together with Cyprus and in contrast most of other destinations in this research. The three countries in this group are also known for favourable tax policies, and this could be of interest to further future research. Conclusions and Further Research This paper presents a preliminary secondary study of various international statistics that measure different aspects of national economies. By combining data about GDP, travel and tourism competitiveness and the number of association events, we were able to confirm the existence of strong connections between some of them. Through cluster analysis, we have identified groups of countries with similarities in the relation between the number of international events and GDP. This paper shows that despite differences between countries, national legislation and strong business presence can attract additional association events, which further strengthens national economies. Despite the failure to prove a direct correlation between the differences in GDP, the three WEF TTCI sub-indexes and the number of association events in a seven-year period, the research has opened various questions. A further collection of data and analysis focusing on comparable data over a longer period could perhaps reveal specific emerging patterns. Unfortunately, the WEF does not perform this analysis on a yearly basis and only the years that were included in the research were usable. The analysed period has been marked by a strong anomaly caused by the global economic crisis and austerity measures, which have strongly affected the countries concerned and the willingness of associations to venture to new European destinations. However, the results still indicate a strong connection between the economy and association meetings, and show that the effects of this lucrative segment of tourism can be felt in the analysed destinations. The research can thus serve DMOs and tourism companies in lobbying and persuading local governments to pass legislation that will strengthen the competitiveness of their destinations. The present research could be extended in the future to focus on the two indexes; more countries could also be included. A major issue of the research was the size of the sample, but this can be corrected in the future by analysing the entire EU or the world. The research can serve as a foundation for the creation of an extended site selection model and the inclusion of country level elements into it. Potentially, it could even reveal systemic weaknesses within countries or even regions. 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